PPT-Intro to GPU s for Parallel Computing

Goals for Rest of Course Learn how to program massively parallel processors and achieve high performance functionality and maintainability scalability across future generations Acquire technical knowledge required to achieve the above goals. Performance Theory - 1. Parallel Computing. CIS . 410/. 510. Department of Computer and Information Science. Outline. Performance scalability. Analytical performance measures. Amdahl. ’. s. law and Gustafson-. Goals for Rest of Course. Learn how to program massively parallel processors and achieve. high performance. functionality and maintainability. scalability across future generations. Acquire technical knowledge required to achieve the above goals. Dr. Guy Tel-. Zur. Lecture 10. Agenda. Administration. Final presentations. Demos. Theory. Next week plan. Home assignment #4 (last). Final Projects. Next Sunday: Groups 1-1. 6. will present. Next Monday: Groups 1. ITS Research Computing. Lani. Clough, Mark Reed. markreed@unc.edu. . Objectives. Introductory. level MATLAB course for people who want to learn . parallel and GPU computing . in MATLAB.. Help participants . Going Beyond Serial MATLAB Applications. MATLAB . Desktop (Client). Worker. Worker. Worker. Worker. Worker. Worker. Programming Parallel Applications (CPU). Built-in support. with t. oolboxes. Ease of Use. with . OpenMP. © Dan Negrut, . 2012. UW-Madison. Dan Negrut. Simulation-Based Engineering . Lab. Wisconsin Applied Computing Center. Department of Mechanical . Engineering. Department of . Electrical and Computer Engineering. Luke Winslow. OWI a-la-carte. Overview. What is Parallel Computing. Example Uses. Intro to Condor. Demo. Parallel Computing. Cluster. Why would I need this?. Dealing with Large Datasets. Lake Shape Characteristics. & Cluster Computing. Stupid Compiler Tricks. Josh Alexander, University of Oklahoma. Ivan . Babic. , Earlham College. Andrew Fitz Gibbon, Shodor Education Foundation Inc.. Henry Neeman, University of Oklahoma. A View from Berkeley. Dave Patterson. Parallel Computing Laboratory. U.C. Berkeley. July, 2008. Outline. What Caused the Revolution?. Is it an Interesting, Important Research Problem or Just Doing Industry’s Dirty Work?. Eileen Berman (stealing from many people). DUNE Collaboration Meeting. Jan 29, 2018. What Does This Include?. Getting Started. LArSoft. . (thanks to Erica Snider). Gallery. . (thanks to Marc . Paterno. How to Use Parallel Computing Toolbox™ and MATLAB® Distributed Computing Server™ on Discovery Cluster, . An EECE5640: High Performance Computing lecture. Benjamin Drozdenko. MathWorks TA & Graduate Research Assistant . Eileen Berman (stealing from many people). DUNE Physics Week. Nov 14, 2017. What Does This Include?. LArSoft. . (thanks to Erica Snider). Gallery. . (thanks to Marc . Paterno. ). Data Management (Storage) . Early Adopter: ASU - Intel Collaboration in Parallel and Distributed Computing Yinong Chen , Eric Kostelich , Yann -Hang Lee, Alex Mahalov , Gil Speyer, and Violet R. Syrotiuk 1 st NSF /TCPP Workshop on Parallel and Distributed Computing Education ( CS 6260. Professor: Elise De . Doncker. By: . Lina. Hussein. 1. Topics Covered :. Introduction. What is cluster computing?. Classification of Cluster Computing. Technologies:. Beowulf cluster. Construction of Beowulf Cluster.